Iris recognition is one of the most popular approaches for non-contact biometric authentication. As presented in K. W. Bowyer, et al., “Image understanding for iris biometrics: A survey,” Computer Vision and Image Understanding, Vol. 110, No. 2, pp. 281-307, 2008, iris patterns are believed to be unique for each person, and remain stable for long periods of time, making them a natural choice as a biometric signature.
In J. R. Matey, et al., “Iris recognition—beyond one meter,” in Handbook of Remote Biometrics (M. Tistarelli, S. Z. Li, and R. Challeppa, eds), Advances in Pattern Recognition, pp. 23-59, Springer London, 2009, there is shown that over the past decade, sensors for acquiring iris patterns have undergone significant transformations, where the existing sensors have been upgraded, and the new ones have been developed. These transformations pose new challenges to iris recognition algorithms. Due to the large number of users, the enrollment process (when enrollment iris samples are acquired, processed, and saved in a database to be retrieved for matching with the test iris samples in the verification process) is expensive and time-consuming. This dictates the re-enrollment of users each time a new sensor is developed.
In practice, situations are often encountered where iris images for enrollment and testing images for verification are acquired by different sensors.
Recent studies in iris biometrics evidence that cross sensor matching, where different sensors are employed for enrollment and testing, often lead to reduced performance. This is illustrated in the diagram presented in FIG. 1 using data obtained with LG2200 and LG4000 sensors. As may be observed, the Receiver Operating Characteristics (ROC) curve (a) of cross-sensor matching is inferior to that of same-sensor matching presented by curves “b” and “c”, respectively. This performance degradation due to the difference in the sensors used at the enrollment process and the testing process is referred to as the “sensor mismatch” problem in iris recognition. Techniques designed to alleviate the sensor mismatch are referred to as “sensor adaptation” methods.
While the sensor mismatch problem has been empirically illustrated by K. Bowyer, et al., in “Factors that degrade the match distribution in iris biometrics,” Identify in the Information Society, Vol. 2, No. 3, pp. 327-343, 2009 and R. Connaughton, et al., “A cross-sensor evaluation of three commercial iris cameras for iris biometrics,” in IEEE Computer Society Workshop on Biometrics, pp. 90-97, 2011, it is believed that no algorithms have been developed as of yet for sensor adaptation specific to iris biometrics.
Due to the different design possibilities and the significant commercial interests in iris recognition, numerous iris acquisition systems are available.
Some of the popular systems are LG2200, LG4000, Iris on the Move portal system by Sarnoff, Combined Face And Iris Recognition System (CFAIRs) by Honeywell, HBOX™ system by Global Rainmakers Inc., and Eagle-Eyes™ system by Retica. The detailed review of the iris recognition systems is well presented in J. R. Matey, et al., “Iris recognition—beyond one meter,” in Handbook of Remote Biometrics (M. Tistarelli, S. Z. Li, and R. Challeppa, eds), Advances in Pattern Recognition, pp. 23-59, Springer London, 2009.
As presented in FIG. 2, the main components of an existing iris recognition system 10, for example, the Daugman's iris recognition system, presented in J. Daugman, “High confidence visual recognition of persons by a test of statistical independence,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 15, No. 11, pp. 1148-1161, 1993, are represented by an image acquisition sub-systems 12 and 12′, iris segmentation sub-systems 14 and 14′, feature extraction sub-systems 16 and 16′, and a template matching sub-system 18.
The iris image acquisition sub-system 12 is used in the iris recognition systems to acquire a sample of an iris image. The image acquisition sub-systems differ mainly in the type and location of the illumination they use, the type of sensor, and the presence of additional optical elements (J. R. Matey, et al., “Iris recognition—beyond one meter,” in Handbook of Remote Biometrics (M. Tistarelli, S. Z. Li, and R. Challeppa, eds), Advances in Pattern Recognition, pp. 23-59, Springer London, 2009). To control the illumination conditions, most commercial systems reject the ambient light and use an active illumination source. Normally, illumination is chosen in the 720-900 nm wavelength, a range where the reflection properties of the melanin pigment on the human iris and the absorption properties of the image sensor used are favorable. Silicon is the popular choice for commercial image sensors, though materials like Germanium with longer wavelengths can also be used.
Most commercial systems use LED illumination since they are small, bright, and simple to control. Lasers, fluorescent lamps, and flash lights are other common active illumination sources. Optical elements, like lenses, are often used to collect and focus light on the image sensor and match the pixel sizes of the human iris and the image sensor.
Iris segmentation sub-systems 14 and 14′ are included in the iris recognition system for finding the pixels in the respective iris image, including enrollment iris image and the test iris image. The iris segmentation involves finding the pupillary and limbic boundaries at the iris image and detecting the occlusion due to eyelids, eyelashes, and specularities. For example, in J. Daugman, “High confidence visual recognition of persons by a test of statistical independence,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 15, No. 11, pp. 1148-1161, 1993, the iris boundaries are approximated as circles and an integro-differential operator is used to find the optimal parameters of these circles. Masks are computed within the two circles to ignore pixels occluded by eyelids and eyelashes.
After the segmentation, the extracted iris region is mapped into a dimensionless coordinate system to normalize the size, and features are extracted in the feature extracting sub-systems 16 and 16′ from the iris region. For example, the Daugman's system uses two-dimensional Gabor filters to capture the texture of the iris region and quantizes the phase response into a pair of bits. This leads to a binary representation of iris texture which is referred to herein as the iris code.
The normalized Hamming distance may be used in the matching sub-system 18 for matching the iris code 20 obtained from the test image with the iris code 22 of the enrollment iris image stored in the gallery, also referred to herein as a database 24 of the enrollment samples representations.
Iris code is the most popular representation for iris biometrics since its matching involves only binary operations, which are extremely efficient. Furthermore, the storage requirements are also significantly reduced by the binary representation of the codes.
In addition to the Gabor filters, numerous other filters are known which have also been used in the iris recognition systems to obtain binary representations from an iris image, including Gaussian filter, dyadic wavelet transform, Laplacian of Gaussian filter, Log-Gabor filters, and Discrete Cosine Transform (DCT), presented in Z. Sun, et al., “Robust Encoding of Local Ordinal measures: A general framework of Iris Recognition” in European Conference of Computer Vision Workshop, pp. 270-282, 2004; L. Ma, et al., “Efficient iris recognition by characterizing key local variations,” IEEE Transactions on Image Processing, Vol. 13, No. 6, pp. 739-750, 2004; C. T. Chou, et al., “Iris recognition with multi-scale edge-type matching,” in International Conference on Pattern Recognition, pp. 545, 548, 2006; P. Yao, et al., “Iris recognition algorithm using modified log-Gabor filters,” in International Conference on Pattern Recognition, pp. 461-464, 2006; as well as D. M. Monro, et al., “Det-based iris recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 4, pp. 586-595, 2007.
Though less popular than the algorithms using binary representation, there exist numerous algorithms for iris recognition using real-valued features. These methods mainly use the output of the wavelet transform applied on the iris region as the feature and the Euclidean distance or Support Vector Machines for matching.
W. W. Boles, et al., in “A human identification technique using images of the iris and wavelet transform,” IEEE Transactions on Signal Processing, Vol. 46, No. 4, pp. 1185-1188, 1998, applied wavelet transform on concentric circular bands of iris pixels and developed dissimilarity functions on the zero crossings of the transform output. J. Gan, et al., in “Applications of wavelet packets decomposition in iris recognition,” in International Conference on Biometrics, pp. 443-449, 2006 used Daubechies-4 wavelet as feature and weighted Euclidean distance for matching statistical techniques including Principal component analysis, Independent Component Analysis, and Linear Discriminant Analysis have also been used to obtain real-valued feature vectors.
Owing to the large number of iris recognition systems currently available and the continuous improvement of existing systems, the inter-operability of iris recognition systems has become extremely important. Several papers have addressed the problem of biometric interoperability for fingerprint sensors, including A. Ross, et al., “Biometric sensor interoperability: A case study in fingerprints,” in International ECCV Workshop on Biometric Authentication, pp. 134-145, 2004; F. Alonso-Fernandez, et al., “Sensor interoperability and fusion in fingerprint verification: A case study using minutiae-and ridge-based matchers,” in International Conference on Control, Automation, Robotics and Vision, pp. 1-6, 2006; and for multi-biometrics systems (F. Alonso-Fernandez, et al., “Quality-based conditional processing in multi-biometrics: Application to sensor interoperability,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 40, No. 6, pp. 1168-1179, 2010).
For face verification, the influence of camera types was identified in P. Phillips, et al., “An introduction evaluating biometric system,” Computer, Vol. 33, pp. 56-63, Feb. 2000. E. Gonzales, et al., “Looking for hand biometrics interoperability,” in Hand-Based Biometrics (ICHB), 2011 International Conference on, pp. 1-6, 2011, developed methods for inter-operability among hand biometric systems.
In iris biometrics, this problem was first investigated by Bowyer et al, in “Factors that degrade the match distribution in iris biometrics,” Identity in the Information Society, Vol. 2, pp. 327-343, 2009, using two iris sensors. Their work demonstrated that the older of the two sensors provided less desirable match score distributions. Furthermore, the cross-sensor performance was inferior to that of either sensors tested individually, as may be observed in FIG. 1.
Cross-sensor iris recognition was further explored by R. Connaughton et al., in “A cross-sensor evaluation of three commercial iris cameras for iris biometrics,” in IEEE Computer Society Workshop on Biometrics, pp. 90-97, 2011, and R. Connaughton, et al., “A multialgorithm analysis of three iris biometric sensors,” IEEE Transactions on Information Forensics and Security, Vol. 7, No. 3, pp. 919-931, 2012, who experimented with three commercially available iris sensors. However, while these methods clearly demonstrate the need for improving the cross-sensor recognition performance, no algorithms have been proposed for handling the sensor mismatch problems.
It is, therefore, would be highly desirable to provide a technique which may be incorporated in the existing iris recognition systems capable of producing comprehensive solution for the sensor mismatch problem to efficiently mitigate the cross sensor performance degradation by adapting the iris samples from one sensor to another.
T. Hofmann, et al., in “Kernel methods in machine learning,” Annals of Statistics, Vol. 36, No. 3, pp. 1120, 2008, presents an extensive description of the topic. The use of kernel methods in the machine learning is motivated by the fact that traditionally, the theory and algorithms for machine learning are well developed, well understood and efficient for linear models. However, non-linear models are often required to describe the data. Instead of explicitly learning a non-linear model, kernel methods project the data into a higher dimensional space and learn linear models in the projected space. By the special construction of these methods, data appear in computation only in the form of inner products, which can be performed without explicit projection into the high dimensional space, using kernel functions.
Kernels incorporate the prior knowledge available about a problem and hence the choice of kernel is very crucial. Existence of a good kernel for a task automatically makes a wide variety of kernel algorithms applicable for that task.
B. E. Boser, et al., “A training algorithm optimal margin classifiers,” in Conference on Learning Theory, pp. 144-152, 1992, introduced kernels into mainstream machine learning literature by combining kernel functions and maximum margin hyperplanes, leading to the well-known Support Vector Machines (SVM). Kernels have also been used for clustering as presented in I. S. Dhillon, et al., “Kernel k-means: spectral clustering and normalized cuts,” in International Conference on Knowledge Discovery and Data Mining, pp. 551-556, 2004; for metric learning, as presented in J. V. Davis, et al., “Information-theoretic metric learning,” in International Conference on Machine Learning, pp. 209-216, 2007; for domain adaptation, as presented in K. Saenko, et al., “Adapting visual category models to new domains,” in European Conference on Computer Vision, pp. 213-226, 2010; for probabilistic regression, as presented in T. Jaakkola, et al., Exploiting generative models in discriminative classifiers,” in Neural and Information Processing Systems, pp. 487-493, 1998; and for dictionary learning, as presented in Kernel Dictionary Learning, 2012.
These advantages of kernel methods have led to the development of specialized kernel functions for applications like text categorization, as presented in H. Lodhi, et al., “Text classification using string kernels,” Journal of Machine Learning Research, Vol. 2, pp. 419-444, 2002. Furthermore, algorithms have also been developed for learning kernel functions having desired properties, as presented in K. Q. Weinberger, et al., “Learning a kernel matrix for nonlinear dimensionality reduction,” in Proceedings of the twenty-first international conference on Machine learning, ICML '04, (New York, N.Y., USA), pp. 106 and further, ACM, 2004.
It is, therefore, would be desirable to use kernel based approach for producing comprehensive solution for the sensor mismatch problems.